Body-worn monitor for measuring respiratory rate

ABSTRACT

The invention provides a system for measuring respiratory rate (RR) from a patient. The system includes an impedance pneumography (IP) sensor, connected to at least two electrodes, and a processing system that receives and processes signals from the electrodes to measure an IP signal. A motion sensor (e.g. an accelerometer) measures at least one motion signal (e.g. an ACC waveform) describing movement of a portion of the patient&#39;s body to which it is attached. The processing system receives the IP and motion signals, and processes them to determine, respectfully, frequency-domain IP and motion spectra. Both spectra are then collectively processed to remove motion components from the IP spectrum and determine RR. For example, during the processing, an algorithm determines motion frequency components from the frequency-domain motion spectrum, and then using a digital filter removes these, or parameters calculated therefrom, from the IP spectrum.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/156,138, filed May 16, 2016, now U.S. Pat. No. 10,390,731, issuedAug. 27, 2019, which is a continuation of U.S. patent application Ser.No. 12/762,952, filed Apr. 19, 2010, now U.S. Pat. No. 9,339,209, issuedMay 17, 2016, each of which is incorporated herein by reference,including all tables and figures.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to medical devices for monitoring vitalsigns, e.g., respiratory rate (RR).

Description of the Related Art

RR is a vital sign typically measured in hospitals using either anindirect, electrode-based technique called ‘impedance pneumography’(IP), a direct optical technique called ‘end-tidal CO2’ (et-CO2), orsimply through manual counting of breaths by a medical professional. IPis typically performed in lower-acuity areas of the hospital, and usesthe same electrodes which measure an electrocardiogram (ECG) andcorresponding heart rate (HR). These electrodes are typically deployedin a conventional ‘Einthoven's triangle’ configuration on the patient'storso. During IP, one of the electrodes supplies a low-amperage (˜4 mA)current that is typically modulated at a high frequency (˜50-100 kHz).Current passes through the patient's thoracic cavity, which ischaracterized by a variable, time-dependent capacitance that varies witheach breath. A second electrode detects current which is modulated bythe changing capacitance. Ultimately this yields an analog signal thatis processed with a series of amplifiers and filters to detect thetime-dependent capacitance change and, with subsequent analysis, thepatient's RR.

In et-CO2, a device called a capnometer features a small plastic tubethat inserts in the patient's mouth. With each breath the tube collectsexpelled CO2. A beam of infrared radiation emitted from an integratedlight source passes through the CO2 and is absorbed in a time-dependentmanner that varies with the breathing rate. A photodetector and seriesof processing electronics analyze the transmitted signal to determineRR. et-CO2 systems are typically used in high-acuity areas of thehospital, such as the intensive care unit (ICU), where patients oftenneed ventilators to assist them in breathing.

In yet another technique, RR can be measured from the envelope of atime-dependent optical waveform called a photoplethysmogram (PPG) thatis measured from the patient's index finger during a conventionalmeasurement of the patient's oxygen saturation (SpO2). Breathing changesthe oxygen content in the patient's blood and, subsequently, its opticalabsorption properties. Such changes cause a slight, low-frequencyvariation in the PPG that can be detected with a pulse oximeter'soptical system, which typically operates at both red and infraredwavelengths.

Not surprisingly, RR is an important predictor of a decompensatingpatient. For example, a study in 1993 concluded that a RR greater than27 breaths/minute was the most important predictor of cardiac arrests inhospital wards (Fieselmann et al., ‘RR predicts cardiopulmonary arrestfor internal medicine patients’, J Gen Intern Med 1993; 8: 354-360).Subbe et al. found that, in unstable patients, relative changes in RRwere much greater than changes in heart rate or systolic blood pressure;RR was therefore likely to be a better means of discriminating betweenstable patients and patients at risk (Subbe et al., ‘Effect ofintroducing the Modified Early Warning score on clinical outcomes,cardio-pulmonary arrests and intensive care utilization in acute medicaladmissions’, Anaesthesia 2003; 58: 797-802). Goldhill et al. reportedthat 21% of ward patients with a RR of 25-29 breaths/minute assessed bya critical care outreach service died in hospital (Goldhill et al., ‘Aphysiologically-based early warning score for ward patients: theassociation between score and outcome’, Anaesthesia 2005; 60: 547-553).Those with a higher RR had even higher mortality rates. In anotherstudy, just over half of all patients suffering a serious adverse eventon the general wards (e.g. a cardiac arrest or ICU admission) had a RRgreater than 24 breaths/minute. These patients could have beenidentified as high risk up to 24 hours before the event with aspecificity of over 95% (Cretikos et al., ‘The Objective MedicalEmergency Team Activation Criteria: a case-control study’, Resuscitation2007; 73: 62-72). Medical references such as these clearly indicate thatan accurate, easy-to-use device for measuring RR is an importantcomponent for patient monitoring within the hospital.

Despite its importance and the large number of available monitoringtechniques, RR is notoriously difficult to measure, particularly when apatient is moving. During periods of motion, non-invasive techniquesbased on IP and PPG signals are usually overwhelmed by artifacts, andthus completely ineffective. This makes it difficult or impossible tomeasure RR from an ambulatory patient. Measurements based on et-CO2 aretypically less susceptible to motion, but require a plastic tubeinserted in the patient's mouth, which is uncomfortable and typicallyimpractical for ambulatory patients.

SUMMARY OF THE INVENTION

This invention provides a technique for measuring RR using multipleinput signals, including IP and accelerometer waveforms (ACC). Afterbeing measured with a body-worn system, an algorithm collectivelyanalyzes these waveforms to determine RR from an ambulatory patientusing combinations of simple peak counting, Fourier Transforms (FFT) andadaptive filtering. The patient's degree of motion determines which ofthese algorithms is implemented: simple peak counting is preferably usedwhen the patient is undergoing no motion, while the FFT-based algorithmis used when motion is extreme. Adaptive filtering is typically usedduring periods of moderate motion. The algorithms are typicallyperformed using a microprocessor, computer code and memory located in awrist-worn transceiver, a sensor module located directly on thepatient's chest, or on a remote server located, e.g., in a hospital.Calculations may be performed in a distributed manner, meaning portionsof them can be performed with a first microprocessor (e.g., the serverin the hospital), resulting in parameters that are then sent to a secondmicroprocessor (e.g., in the wrist-worn transceiver) for finalprocessing. Such a distributed model can reduce the computational burdenon microprocessors within the body-worn monitor, thereby conservingpower and extending battery life.

The accelerometer is typically mounted on the patient's torso (mosttypically the chest or belly), and measures small, breathing-inducedmovements to generate the time-dependent ACC waveform. The ACC waveformis also highly sensitive to the patient's motion and position, and thusthe ACC waveform can be processed to determine parameters such as degreeof motion, posture, and activity level. With the FFT-based algorithms,time-domain ACC and IP waveforms are mathematically transformed to thefrequency domain and processed to generate a power spectrum. Furtherprocessing of this signal yields frequency components corresponding toboth respiratory events and motion. The ACC waveform yields well-definedfrequency components that are highly sensitive to motion. These signalscan be collectively processed and used to filter out motion artifactsfrom the transformed IP waveform. The resulting power spectrum is thenfurther processed with a smoothing function, yielding a set offrequency-domain peaks from which RR can be accurately calculated.

The multi-component algorithm also processes both IP and ACC waveformsto determine parameters for an adaptive filtering calculation. Once theparameters are determined, this filter is typically implemented with afinite impulse response (FIR) function. Ultimately this yields acustomized filtering function which then processes the IP waveform togenerate a relatively noise-free waveform with well-defined pulsescorresponding to RR. Each pulse can then be further processed andcounted to determine an accurate RR value, even during periods ofmotion.

The body-worn monitor measures IP and ACC waveforms as described above,along with PPG and ECG waveforms, using a series of sensors integratedinto a comfortable, low-profile system that communicates wirelessly witha remote computer in the hospital. The body-worn monitor typicallyfeatures three accelerometers, each configured to measure a uniquesignal along its x, y, and z axes, to yield a total of nine ACCwaveforms. Typically the accelerometers are embedded in the monitor'scabling or processing unit, and are deployed on the patient's torso,upper arm, and lower arm. Each ACC waveform can be additionallyprocessed to determine the patient's posture, degree of motion, andactivity level. These parameters serve as valuable information that canultimately reduce occurrences of ‘false positive’ alarms/alerts in thehospital. For example, if processing of additional ACC waveformsindicates a patient is walking, then their RR rate, which may beaffected by walking-induced artifacts, can be ignored by an alarm/alertengine associated with the body-worn monitor. The assumption in thiscase is that a walking patient is likely relatively healthy, regardlessof their RR value. Perhaps more importantly, with a conventionalmonitoring device a walking patient may yield a noisy IP signal that isthen processed to determine an artificially high RR, which then triggersa false alarm. Such a situation can be avoided with an independentmeasurement of motion, such as that described herein. Other heuristicrules based on analysis of ACC waveforms may also be deployed accordingto this invention.

Sensors attached to the wrist and bicep each measure signals that arecollectively analyzed to estimate the patient's arm height; this can beused to improve accuracy of a continuous blood pressure measurement(cNIBP), as described below, that measures systolic (SYS), diastolic(DIA), and mean (MAP) arterial blood pressures. And the sensor attachedto the patient's chest measures signals that are analyzed to determineposture and activity level, which can affect measurements for RR, SpO2,cNIBP, and other vital signs. Algorithms for processing information fromthe accelerometers for these purposes are described in detail in thefollowing patent applications, the contents of which are fullyincorporated herein by reference: BODY-WORN MONITOR FEATURING ALARMSYSTEM THAT PROCESSES A PATIENT'S MOTION AND VITAL SIGNS (U.S. Ser. No.12/469,182; filed May 20, 2009) and BODY-WORN VITAL SIGN MONITOR WITHSYSTEM FOR DETECTING AND ANALYZING MOTION (U.S. Ser. No. 12/469,094;filed May 20, 2009). As described therein, knowledge of a patient'smotion, activity level, and posture can greatly enhance the accuracy ofalarms/alerts generated by the body-worn monitor.

The body-worn monitor features systems for continuously monitoringpatients in a hospital environment, and as the patient transfers fromdifferent areas in the hospital, and ultimately to the home. Both SpO2and cNIBP rely on accurate measurement of PPG and ACC waveforms, alongwith an ECG, from patients that are both moving and at rest. cNIBP istypically measured with the ‘Composite Technique’, which is described indetail in the co-pending patent applications entitled: VITAL SIGNMONITOR FOR MEASURING BLOOD PRESSURE USING OPTICAL, ELECTRICAL, ANDPRESSURE WAVEFORMS (U.S. Ser. No. 12/138,194; filed Jun. 12, 2008) andBODY-WORN SYSTEM FOR MEASURING CONTINUOUS, NON-INVASIVE BLOOD PRESSURE(CNIBP) (U.S. Ser. No. 12/650,354; filed Nov. 15, 2009), the contents ofwhich are fully incorporated herein by reference.

As described in these applications, the Composite Technique (or,alternatively, the ‘Hybrid Technique’, as referred to therein) typicallyuses a single PPG waveform from the SpO2 measurement (typicallygenerated with infrared radiation), along with the ECG waveform, tocalculate a parameter called ‘pulse transit time’ (PTT) which stronglycorrelates to blood pressure. Specifically, the ECG waveform features asharply peaked QRS complex that indicates depolarization of the heart'sleft ventricle, and, informally, provides a time-dependent marker of aheart beat. PTT is the time separating the peak of the QRS complex andthe onset, or ‘foot’, of the PPG waveforms. The QRS complex, along withthe foot of each pulse in the PPG, can be used to more accuratelyextract AC signals using a mathematical technique described in detailbelow. In other embodiments both the red and infrared PPG waveforms arecollectively processed to enhance the accuracy of the cNIBP measurement.

The electrical system for measuring IP and ACC waveforms is featured ina sensor module that connects to an end of a cable that terminates inthe wrist-worn transceiver, and is mounted directly on the patient'schest. The sensor module measures high-fidelity digital waveforms whichpass through the cable to a small-scale, low-power circuit mounted on acircuit board that fits within the transceiver. There, an algorithmprocesses the two waveforms using the multi-component algorithm todetermine RR. The transceiver additionally includes a touchpaneldisplay, barcode reader, and wireless systems for ancillary applicationsdescribed, for example, in the above-referenced applications, thecontents of which have been previously incorporated herein by reference.

In one aspect, the invention features a system for measuring RR from apatient. The system includes an IP sensor, connected to at least twoelectrodes, and a processing system that receives and processes signalsfrom the electrodes to measure an IP signal. The electrodes can connectto the IP sensor through either wired or wireless means. A motion sensor(e.g. an accelerometer) measures at least one motion signal (e.g. an ACCwaveform) describing movement of a portion of the patient's body towhich it is attached. The processing system receives the IP and motionsignals, and processes them to determine, respectfully, frequency-domainIP and motion spectra. Both spectra are then collectively processed toremove motion components from the IP spectrum and determine RR. Forexample, during the processing, an algorithm determines motion frequencycomponents from the frequency-domain motion spectrum, and then using adigital filter removes these, or parameters calculated therefrom, fromthe IP spectrum.

In embodiments, a single sensor module, adapted to be worn on thepatient's torso, encloses both the IP sensor and the motion sensor. Thesensor module typically includes at least one analog-to-digitalconverter configured to digitize the IP signal; this component may beintegrated directly into a single-chip circuit (e.g. anapplication-specific integrated circuit, or ASIC), or in a circuitconsisting of a collection of discrete components (e.g. individualresistors and capacitors). The sensor module can also include at leastone analog-to-digital converter configured to digitize the motionsignal. Similarly, this component can be integrated directly into theaccelerometer circuitry. Digitizing the IP and motion signals beforetransmitting them to the processing system has several advantages, asdescribed in detail below.

In other embodiments, the sensor module includes a temperature sensorfor measuring the patient's skin temperature, and an ECG circuit(corresponding to a three, five, or twelve-lead ECG) for measuring anECG waveform. In embodiments, the sensor module simply rests on thepatient's chest during a measurement, or can be connected with a smallpiece of medical tape. Alternatively, the housing features a connectorthat connects directly to an ECG electrode worn on the patient's torso.

The processing system is typically worn on the patient's wrist.Alternatively, this system can be within the sensor module, or within aremote computer server (located, e.g., in a hospital's IT system).Typically a wireless transceiver (e.g. a transceiver based on 802.11 or802.15.4 transmission protocols) is included in the system, typicallywithin the processing module. Such a transceiver, for example, canwirelessly transmit IP and ACC waveforms to a remote processing systemfor further analysis. In this case, the processing system is furtherconfigured to wireless transmit a RR value back to a second processorworn on the patient's body, where it can then be displayed (using, e.g.,a conventional display).

Accelerometers used within the system typically generate a unique ACCwaveform corresponding to each axis of a coordinate system. Inembodiments the system can include three accelerometers, each worn on adifferent portion of the patient's body. Waveforms generated by theaccelerometers can then be processed as described in detail below todetermine the patient's posture.

In another aspect, the invention features an algorithm, typicallyimplemented using compiled computer code, a computer memory, and amicroprocessor, that processes IP and ACC waveforms by calculating theirpower spectra by way of a Fourier transform (e.g. a fast Fouriertransform, of FFT). The algorithm then determines motion components fromthe frequency-dependent ACC spectrum, and using a digital filter removesthese, or components calculated therefrom, from the frequency-dependentIP spectrum. This yields a processed, frequency-dependent IP spectrumwhich can then be analyzed as described in detail below to estimate RR,even when large amounts of motion-induced noise are evident on the IPsignal.

In embodiments, power spectra for both the IP and ACC waveforms arecalculated from a complex FFT that includes both real and imaginarycomponents. In other embodiments, alternative mathematical transforms,such as a Laplace transform, can be used in place of the FFT.

To determine a digital filter from the ACC power spectrum, the algorithmtypically includes a method for first finding a peak corresponding toone or more frequencies related to the patient's motion. A bandpassfilter, characterized by a passband which filters out these (andrelated) frequencies, is then generated and used to process the IPspectrum. Alternatively, these frequencies can simply be divided orsubtracted from the IP spectrum. In all cases, this yields a processedIP spectrum which is then further analyzed to determine a frequencycorresponding to RR. Analysis can include smoothing, averaging, orrelated methodologies uses to extract a single frequency, correspondingto RR, from a collection of frequencies. In embodiments, the algorithmcan also include a component that generates an alarm if the patient's RRis greater than a first pre-determined threshold, or less than a secondpre-determined threshold. The alarm can be generated by considering boththe patient's RR and posture.

In another aspect, the invention features a multi-component algorithmfor determining RR. The multi-component algorithm first determines amotion parameter from the ACC waveform. The motion parameter indicatesthe patient's degree of motion, activity level, or posture. Based on themotion parameter, the multi-component algorithm then selects one of thefollowing algorithms to process one or both of the ACC and IP waveformsto determine RR: i) a first algorithm featuring countingbreathing-induced pulses in the IP waveform; and ii) a second algorithmfeaturing collectively processing both the ACC and IP waveform todetermine a digital adaptive filter, and then processing one of thesewaveforms with the adaptive filter to determine RR; and iii) a thirdalgorithm featuring mathematically transforming both the ACC and IPwaveforms into frequency-domain spectra, and then collectivelyprocessing the spectra to determine RR. Typically the first, second, andthird algorithms are deployed, respectively, when the motion parameterindicates the patient's motion is non-existent, minimal, or large. Forexample, the first algorithm is typically deployed when the patient isresting; the second algorithm deployed when the patient is moving aboutsomewhat; and the third algorithm deployed when the patient is standingup, and possibly walking or even running.

In another aspect, the multi-component algorithm is deployed on one ormore microprocessors associated with the system. For example, toconserve battery life of the body-worn monitor, numerically intensivecalculations (such as the FFT or those used to generate the digitalfilter) can be performed on a remote server; intermediate or finalparameters associated with these calculations can then be wirelesslytransmitted back to the body-worn monitor for further processing ordisplay. In another embodiment, portions of the multi-componentalgorithm can be carried out by microprocessors located in both thewrist-worn transceiver and chest-worn sensor module. The microprocessorscan communicate through serial or wireless interfaces. This latterapproach will have little impact on battery life, but can reduceprocessing time by simultaneously performing different portions of thecalculation.

In another aspect, the invention provides a method for measuring RR froma patient using an algorithm based on a digital adaptive filter. In thisapproach, the body-worn monitor measures both IP and ACC waveforms asdescribed above. The waveforms are then collectively processed todetermine a set of coefficients associated with the adaptive filter.Once calculated, the coefficients are stored in a computer memory. At alater point in time, the monitor measures a second set of IP and ACCwaveforms, and analyzes these to determine a motion parameter. When themotion parameter exceeds a pre-determined threshold, the algorithmprocesses the set of coefficients and the latest IP waveform todetermine a processed IP waveform, and then analyzes this to determineRR.

In embodiments, the digital adaptive filter is calculated from animpulse response function, which in turn is calculated from either a FIRfunction or an autoregressive moving average model. The order of theadaptive filter calculated from the impulse response function istypically between 20 and 30, while the order of the filter calculatedfrom the autoregressive moving average model is typically between 1 and5. A specific mathematic approach for calculating the digital adaptivefilter is described below with reference to Eqs. 1-16. As describedabove, the coefficients can be calculated using a microprocessor locatedon the wrist-worn transceiver, sensor module, or a remote server.

In yet another aspect, the invention provides a system for measuring RRfeaturing a sensor module configured to be worn on the patient's torso.The sensor module includes sensors for measuring both IP and ACCwaveforms, and a serial transceiver configured to transmit the digitalsignals through a cable to a wrist-worn processing system. This systemfeatures a connector that receives the cable, and a processor thatreceives digital signals from the cable and collectively processes themwith a multi-component algorithm to determine RR. In embodiments, thesensor module digitally filters the IP and ACC waveforms before theypass through the cable. The cable can also include one or more embeddedaccelerometers, and is configured to attach to the patient's arm.

In all embodiments, the wrist-worn transceiver can include a displayconfigured to render the patient's RR and other vital signs, along witha touchpanel interface. A wireless transceiver within the wrist-worntransceiver can transmit information to a remote computer usingconventional protocols such as 802.11, 802.15.4, and cellular (e.g. CDMAor GSM). The remote computer, for example, can be connected to ahospital network. It can also be a portable computer, such as a tabletcomputer, personal digital assistant, or cellular phone.

Many advantages are associated with this invention. In general, itprovides an accurate measurement of RR, along with an independentmeasurement of a patient's posture, activity level, and motion. Theseparameters can be collectively analyzed to monitor a hospitalizedpatient and improve true positive alarms while reducing the occurrenceof false positive alarms. Additionally, the measurement of RR isperformed with a body-worn monitor that is comfortable, lightweight, andlow-profile, making it particularly well suited for ambulatory patients.Such a monitor could continuously monitor a patient as, for example,they transition from the emergency department to the ICU, and ultimatelyto the home after hospitalization.

Still other embodiments are found in the following detailed descriptionof the invention and in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic view of a patient wearing a sensor module ontheir chest that connects to three electrodes arranged in an Einthoven'striangle configuration and measures both ACC and IP waveforms;

FIG. 2 shows a schematic view of a patient wearing a sensor module ontheir belly that connects to three electrodes arranged in an Einthoven'striangle configuration and measures both ACC and IP waveforms;

FIG. 3A is a schematic view of a patient wearing an alternate embodimentof the invention featuring a sensor module for measuring IP and ACCwaveforms that connects directly through an electrode to the patient'sbelly;

FIG. 3B is a schematic, cross-sectional view of the sensor module ofFIG. 3A connected to the patient's belly with the electrode;

FIGS. 4A and 4B show, respectively, a three-dimensional image of thebody-worn monitor of the invention attached to a patient during andafter an initial indexing measurement. FIG. 4A shows the system usedduring the indexing portion of the Composite Technique, and includes apneumatic, cuff-based system 85. FIG. 4B shows the system used forsubsequent RR and cNIBP measurements;

FIG. 5 shows a three-dimensional image of the wrist-worn transceiverused with the body-worn monitor from FIGS. 4A and 4B;

FIG. 6 shows a schematic view of a multi-component algorithm used tocollectively process ACC and IP waveforms to measure RR according to theinvention;

FIG. 7 shows a schematic drawing of Algorithm 1 used in themulti-component algorithm of FIG. 6;

FIG. 8 shows a schematic drawing of computation steps used in Algorithm1 to calculate RR;

FIG. 9 shows a series of time-dependent IP waveforms (left-hand side)and their corresponding mathematical derivatives (right-hand side)measured from a slowly breathing patient and processed with digitalfilters featuring gradually decreasing passbands;

FIG. 10 shows a series of time-dependent IP waveforms (left-hand side)and their corresponding mathematical derivatives (right-hand side)measured from a rapidly breathing patient and processed with digitalfilters featuring gradually decreasing passbands;

FIG. 11 shows a schematic drawing of Algorithm 2 used in themulti-component algorithm of FIG. 6 to calculate RR;

FIG. 12 shows a schematic drawing of Algorithm 3 used in themulti-component algorithm of FIG. 6 to calculate RR;

FIG. 13 shows a schematic drawing of computation steps used inAlgorithms 2 and 3 to calculate RR;

FIG. 14 shows a schematic drawing of a flow chart of computation stepsused to calculate coefficients for adaptive filtering which are used inAlgorithms 2 and 3 to calculate RR;

FIG. 15A shows a graph of an ACC waveform filtered initially with a0.01→2 Hz bandpass filter. FIG. 15B shows a graph of an IP waveformfiltered initially with a 0.01→12 Hz bandpass filter. FIG. 15C shows agraph of an IP waveform adaptively filtered with a bandpass filterranging from 0.01 Hz to 1.5 times the breathing rate calculated from theACC waveform in FIG. 15A. FIG. 15D shows a graph of a first derivativeof the filtered IP waveform in FIG. 15C. FIG. 15E shows a graph of theadaptively filtered IP waveform in FIG. 15C along with markersindicating slow, deep breaths as determined from the algorithm shown bythe flow chart in FIG. 14. FIG. 15F is a flow chart showing thealgorithmic steps used to process the waveforms shown in FIG. 15A-E;

FIG. 16A shows a graph of an ACC waveform filtered initially with a0.01→2 Hz bandpass filter. FIG. 16B shows a graph of an IP waveformfiltered initially with a 0.01→12 Hz bandpass filter. FIG. 16C shows agraph of an IP waveform adaptively filtered with a bandpass filterranging from 0.01 Hz to 1.5 times the breathing rate calculated from theACC waveform in FIG. 16A. FIG. 16D shows a graph of a first derivativeof the filtered IP waveform in FIG. 16C. FIG. 16E shows a graph of theadaptively filtered IP waveform in FIG. 16C along with markersindicating fast, deep breaths as determined from the algorithm shown bythe flow chart in FIG. 14. FIG. 16F is a flow chart showing thealgorithmic steps used to process the waveforms shown in FIG. 16A-E.

FIG. 17A shows a graph of an ACC waveform filtered initially with a0.01→2 Hz bandpass filter. FIG. 17B shows a graph of an IP waveformfiltered initially with a 0.01→12 Hz bandpass filter. FIG. 17C shows agraph of an IP waveform adaptively filtered with a bandpass filterranging from 0.01 Hz to 1.5 times the breathing rate calculated from theACC waveform in FIG. 17A. FIG. 17D shows a graph of a first derivativeof the filtered IP waveform in FIG. 17C. FIG. 17E shows a graph of theadaptively filtered IP waveform in FIG. 17C along with markersindicating very fast, deep breaths as determined from the algorithmshown by the flow chart in FIG. 14. FIG. 17F is a flow chart showing thealgorithmic steps used to process the waveforms shown in FIG. 17A-E.

FIG. 18 shows a schematic drawing of Algorithm 4 used in themulti-component algorithm of FIG. 6 to calculate RR;

FIG. 19 shows a schematic drawing of computation steps used in Algorithm4 to calculate RR;

FIG. 20A shows a time-domain IP waveform measured from a runningpatient.

FIG. 20B shows a time-domain ACC waveform simultaneously measured fromthe same patient. FIG. 20C shows a frequency-domain power spectrum ofthe IP waveform of FIG. 20A. FIG. 20D shows a frequency-domain powerspectrum of the ACC waveform of FIG. 20B. FIG. 20E shows thefrequency-domain power spectrum of the IP waveform of FIG. 20C processedwith a notch filter. FIG. 20F shows the frequency-domain power spectrumof the IP waveform of FIG. 20E processed with a smoothing filter.

FIG. 21A shows a time-domain IP waveform measured from a walkingpatient.

FIG. 21B shows a time-domain ACC waveform simultaneously measured fromthe same patient. FIG. 21C shows a frequency-domain power spectrum ofthe IP waveform of FIG. 21A. FIG. 21D shows a frequency-domain powerspectrum of the ACC waveform of FIG. 21B. FIG. 21E shows thefrequency-domain power spectrum of the IP waveform of FIG. 21C processedwith a notch filter. FIG. 21F shows the frequency-domain power spectrumof the IP waveform of FIG. 21E processed with a smoothing filter.

FIG. 22A shows a time-domain IP waveform measured from a stationarypatient laying down on their back and breathing normally. FIG. 22B showsa time-domain ACC waveform measured simultaneously from the samepatient. FIG. 22C shows a frequency-domain power spectra of both thetime-domain IP waveform of FIG. 22A and ACC waveform of FIG. 22B.

FIG. 23A shows a time-domain IP waveform measured from a stationarypatient laying down on their back and breathing rapidly. FIG. 23B showsa time-domain ACC waveform measured simultaneously from the samepatient. FIG. 23C shows frequency-domain power spectra of both thetime-domain IP waveform of FIG. 23A and ACC waveform of FIG. 23B.

FIG. 24A shows a time-domain IP waveform measured from a stationarypatient laying face down and breathing normally. FIG. 24B shows atime-domain ACC waveform measured simultaneously from the same patient.FIG. 24C shows and frequency-domain power spectra of both thetime-domain IP waveform of FIG. 24A and ACC waveform of FIG. 24B.

FIG. 25A shows a frequency-domain power spectrum of an IP waveformprocessed with no smoothing filter. FIG. 25B shows a frequency-domainpower spectrum of an IP waveform processed with a 5.0 Hz smoothingfilter. FIG. 25C shows a frequency-domain power spectrum of an IPwaveform processed with a 2.5 Hz smoothing filter. FIG. 25D shows afrequency-domain power spectrum of an IP waveform processed with a 1.0Hz smoothing filter. FIG. 25E shows a frequency-domain power spectrum ofan IP waveform processed with a 0.5 Hz smoothing filter.

FIG. 26A shows a frequency-domain power spectrum of an IP waveformprocessed with no running average. FIG. 26B shows a frequency-domainpower spectrum of an IP waveform processed with a 10-point runningaverage. FIG. 26C shows a frequency-domain power spectrum of an IPwaveform processed with a 20-point running average. FIG. 26D shows afrequency-domain power spectrum of an IP waveform processed with a50-point running average. FIG. 26E shows a frequency-domain powerspectrum of an IP waveform processed with a 100-point running average.

FIG. 27A shows a time-domain IP waveform measured from a rapidlybreathing stationary patient. FIG. 27B shows a frequency-domain powerspectra calculated from the time-domain IP waveform of FIG. 27A using a1000-point FFT. FIG. 27C shows a frequency-domain power spectracalculated from the time-domain IP waveform of FIG. 27A using a500-point FFT. FIG. 27D shows a frequency-domain power spectracalculated from the time-domain IP waveform of FIG. 27A using a250-point FFT.

FIG. 28A shows a time-domain IP waveform measured from a slowlybreathing stationary patient. FIG. 28B shows frequency-domain powerspectra calculated from the time-domain IP waveform of FIG. 28A using a1000-point FFT. FIG. 28C shows frequency-domain power spectra calculatedfrom the time-domain IP waveform of FIG. 28A using a 500-point FFT. FIG.28D shows frequency-domain power spectra calculated from the time-domainIP waveform of FIG. 28A using and a 250-point FFT.

FIG. 29 shows a schematic view of the patient of FIG. 1 and a coordinateaxis used with an algorithm and ACC waveforms to determine the patient'sposture;

FIG. 30A shows a graph of time-dependent ACC waveforms measured from apatient's chest during different postures; and

FIG. 30B shows a graph of time-dependent postures determined byprocessing the ACC waveforms of FIG. 30A with an algorithm andcoordinate axis shown in FIG. 29.

DETAILED DESCRIPTION OF THE INVENTION

Sensor Configuration

Referring to FIGS. 1 and 2, a sensor module 25 featuring an IP circuit27 and accelerometer 12 is mounted on the chest of a patient 10 tosimultaneously measure IP and ACC waveforms. A multi-componentalgorithm, featuring specific algorithms based on simple peak counting,FFT analysis, and adaptive filters processes these waveforms toaccurately measure RR even when the patient 10 is moving. During ameasurement, both the IP 27 and an ECG circuit 26 within the sensormodule connect to a trio of electrodes 20, 22, 24 typically positionedon the patient's torso in an Einthoven's triangle configuration. Eachelectrode 20, 22, 24 measures a unique analog signal that passes througha shielded cable to the ECG circuit 26. This component typicallyincludes a differential amplifier and a series of analog filters withpassbands that pass the high and low-frequency components thatcontribute to the ECG waveform, but filter out components associatedwith electrical and mechanical noise. To determine RR, the IP circuit 27generates a low-amperage current (typically 1-4 mA) that is modulated ata high frequency (typically 50-100 kHz). The current typically passesthrough electrode LL (‘lower left’) 24, which is located on the lowerleft-hand side of the patient's torso. It then propagates through thepatient's chest, as indicated by the arrow 29, where arespiration-induced capacitance change modulates it according to the RR.Electrode UR (‘upper right’) 20 detects the resultant analog signal,which is then processed with a separate differential amplifier andseries of analog filters within the IP circuit to determine an analog IPwaveform featuring a low-frequency series of pulses corresponding to RR.Typically the analog filters in the IP circuit 27 are chosen to filterout high-frequency components that contribute to the ECG QRS complex.

The accelerometer 12 mounted within the sensor module 25 measures ACCwaveforms that are modulated by the patient's general motion andposture, along with small respiratory-induced motions of the patient'storso. The accelerometer 12 simultaneously measures acceleration (e.g.motion) along x, y, and z axes of a local coordinate system, such asthat shown in FIG. 29. As shown in this figure, and described in moredetail below, the accelerometer 12 is preferably aligned so the z axispoints into the patient's torso. Within the accelerometer 12 is aninternal analog-to-digital converter that generates a digital ACCwaveform corresponding to each axis.

Also within the sensor module 25 is a microprocessor 33 andanalog-to-digital converter (not shown in the figure) that digitize theIP and ACC waveforms, and sends them through a serial protocol (e.g. thecontrol area network, or CAN protocol) to the wrist-worn transceiver forfurther processing. There, IP and ACC waveforms are processed with themulti-component algorithm to determine the patient's RR. Alternatively,the algorithms can be performed in part with a remote server, or withthe microprocessor 33 mounted within the sensor module. Additionalproperties such as the patient's posture, degree of motion, and activitylevel are determined from these same digital ACC waveforms. The axiswithin the accelerometer's coordinate system that is aligned along thepatient's torso (and thus orthogonal to their respiration-induced torsomovement) is typically more sensitive to events not related torespiration, e.g. walking and falling.

In a preferred embodiment, digital accelerometers manufactured by AnalogDevices (e.g. the ADXL345 component) are used in the configuration shownin FIG. 1. These sensors detect acceleration over a range of +/−2 g (or,alternatively, up to +/−8 g) with a small-scale, low-power circuit.

Many patient's are classified as ‘belly breathers’, meaning duringrespiration their belly undergoes larger movements than their chest. Arelative minority of patients are ‘chest breathers’, indicating that itis the chest that undergoes the larger movements. For this reason it ispreferred that RR is determined using an ACC waveform detected along thez-axis with an accelerometer positioned on the patient's belly. Inalternate configurations, a separate accelerometer mounted on the chestcan be used in its place or to augment data collected with thebelly-mounted sensor. Typically, ACC waveforms along multiple axes (e.g.the x and y-axes) are also modulated by breathing patterns, and can thusbe used to estimate RR. In still other configurations multiple signalsfrom one or more accelerometers are collectively processed to determinea single ‘effective’ ACC waveform representing, e.g., an average ofmultiple ACC waveforms. This waveform is then processed as describedherein to determine the patient's RR.

In other embodiments, the sensor module 25 includes a temperature sensor34, such as a conventional thermocouple, that measures the skintemperature of the patient's chest. This temperature is typically a fewdegrees lower than conventional core temperature, usually measured witha thermometer inserted in the patient's throat or rectum. Despite thisdiscrepancy, skin temperature measured with the temperature sensor 34can be monitored continuously and can therefore be used along with RRand other vital signs to predict patient decompensation.

In a preferred embodiment, both the ECG and IP waveforms are generatedwith a single ASIC, or alternatively with a circuit composed of a seriesof discrete elements which are known in the art. The ASIC has anadvantage in that it is a single chip and is included in a circuit thattypically contains fewer electrical components, is relatively small, andis typically very power efficient. In either embodiment, the ECG circuittypically includes an internal analog-to-digital converter thatdigitizes both waveforms before transmission to the wrist-worntransceiver for further processing.

Transmission of digital IP, ECG, and ACC waveforms, along with processedRR values, has several advantages over transmission of analog waveforms.First, a single transmission line in the monitor's cabling can transmitmultiple digital waveforms, each generated by different sensors. Thisincludes multiple ECG waveforms (corresponding, e.g., to vectorsassociated with three, five, and twelve-lead ECG systems) from the ECGcircuit 26, the IP waveform from the IP circuit 27, and ACC waveformsassociated with the x, y, and z axes of accelerometers 10, 12 attachedto the patient's chest. Limiting the transmission line to a single cablereduces the number of wires attached to the patient, thereby decreasingthe weight and cable-related clutter of the body-worn monitor. Second,cable motion induced by an ambulatory patient can change the electricalproperties (e.g. electrical impendence) of its internal wires. This, inturn, can add noise to an analog signal and ultimately the vital signcalculated from it. A digital signal, in contrast, is relatively immuneto such motion-induced artifacts. More sophisticated ECG circuits canplug into the wrist-worn transceiver to replace the three-lead systemshown in FIGS. 1 and 2. These ECG circuits, for example, can include,e.g., five and twelve leads.

Digital data streams are typically transmitted to the wrist-worntransceiver using a serial protocol, such as the CAN protocol, USBprotocol, or RS-232 protocol. CAN is the preferred protocol for thebody-worn monitor described in FIGS. 4A, 4B.

Multi-Component Algorithm for Determining RR

FIG. 6 shows an overview of a multi-component algorithm 149, implementedwith the body-worn monitor shown in FIGS. 4A, 4B, and 5 and anaccompanying server, which determines a patient's RR according to theinvention. The algorithm features the following components fordetermining RR from a hospitalized patient undergoing different levelsof motion:

-   -   Algorithm 1—simple peak-counting calculation implemented on the        wrist-worn transceiver; used when patient motion is minimal or        non-existent    -   Algorithm 2—adaptive filtering calculation with filtering        parameters calculated on the wrist-worn transceiver; used when        some patient motion is evident    -   Algorithm 3—adaptive filtering calculation with filtering        parameters calculated on the server and then transmitted to the        wrist-worn transceiver; used when some patient motion is evident    -   Algorithm 4—FFT-based calculation with active noise        cancellation, performed on the server with processed data        transmitted to the wrist-worn transceiver; used when large        amounts of patient motion is evident

Each of these algorithms, along with both respiratory and motion data tosupport them, are described in more detail below.

Algorithms 1-4—Simultaneous Determination of Motion and RespiratorySignals

Referring again to FIG. 6, before Algorithms 1-4 are implemented, thebody-worn monitor collects ECP, PPG, IP, and ACC waveforms (step 150)from the patient, as described above with references to FIGS. 1-5. Apatient's degree of motion (step 152) and their posture (step 154) aredetermined by processing the ACC waveforms using algorithms described indetail in the following pending patent applications, the contents ofwhich have been previously incorporated herein by reference: BODY-WORNMONITOR FEATURING ALARM SYSTEM THAT PROCESSES A PATIENT'S MOTION ANDVITAL SIGNS (U.S. Ser. No. 12/469,182; filed May 20, 2009) and BODY-WORNVITAL SIGN MONITOR WITH SYSTEM FOR DETECTING AND ANALYZING MOTION (U.S.Ser. No. 12/469,094; filed May 20, 2009). FIGS. 29 and 30, below,further indicate how processing of ACC waveforms yields both posture,degree of motion, and activity level.

The multi-component algorithm 149 processes these motion-relatedproperties to determine which of the four above-described algorithms toimplement. The selected algorithm then simultaneously processes ECG andACC waveforms to determine RR. As described above, motion cansignificantly complicate determination of RR, as these two signals oftenoccur at similar frequencies (typically 0.1-2 Hz), and feature signalcomponents (e.g. ‘pulses’ due to breathing and walking) that looksimilar and themselves are composed of common frequency components. Inaddition to RR, the body-worn monitor calculates cNIBP using the ECG andPPG waveforms, and SpO2 from PPG waveforms generated simultaneously withboth red and infrared light sources, as described above. HR iscalculated from the ECG waveform using methods known in the art.

Both high-level and detailed aspects of Algorithms 1-4 are describedbelow.

Algorithm 1—Peak Counting

Algorithm 1 (step 162) is implemented after determining that the patientis supine and undergoing minimal or no motion (step 156). Here, it isassumed that the IP waveform used to determine RR is not significantlycorrupted by motion, and thus RR can be calculated with relativelysimple means. Put another way, in this case there is minimal couplingbetween the ACC and IP waveforms; collective processing of thesewaveforms to remove motion-related artifacts, as is done with Algorithms2-4, is therefore not required. Algorithm 1 typically yields a highlyaccurate RR, and because of its simplicity requires relatively fewcomputational cycles. This in turn reduces power consumption andprolongs battery lifetime on the wrist-worn transceiver.

FIG. 7 shows a high-level diagram describing Algorithm 1. In this case,all calculations are performed with a microprocessor on the wrist-worntransceiver 172 to yield final values for RR. Once determined, awireless system on the transceiver sends these values to a remote server170 for further processing, display, storage, and incorporation into ahospital's medical records system. The wrist-worn transceiver 172additionally displays RR values on the transceiver's touchpanel displayso that they can be viewed at the patient's bedside. FIG. 8 shows thespecific details of this calculation. It begins by confirming that thepatient is supine and not moving (step 180), which as described above isaccomplished by processing ACC signals generated by the three 3-axisaccelerometers integrated in the body-worn monitor. The IP waveform isthen digitally filtered with a 2.5 Hz digital bandpass filter to removeextraneous noise (typically from electrical and mechanical sources) thatcomplicates processing of the waveform. In typical applications, thedigital filter features a second-order infinite impulse response (IIR).In order to remove any phase distortion, the IIR filter is executed inboth the forward and reverse directions. The reverse filtering stepdoubles the effective order of the filter, and cancels out any phasedistortion introduced by the forward filtering operation. The reversefiltering step is implemented by executing the standard IIR differenceequation, performing a time-reversal on the outputted data, and thenexecuting the same IIR difference equation. While effective in removingphase distortion, such additional steps require an extra differencecomputation which cannot be performed in real-time on a stream of data.This, in turn, increases power consumption in the wrist-worntransceiver, and thus shortens battery life.

FIG. 9 shows how filtering raw IP waveforms with different passbandsultimately affects the signal-to-noise ratio of these signals and theircorresponding derivatives. Ultimately, it is these derivatives that areprocessed to determine RR.

Referring again to FIG. 8, the digitally filtered IP waveform is thenderivatized and squared using Algorithm 1 (step 184), yielding a signalsimilar to that shown in the right-hand side of FIG. 9. Taking aderivative removes any low-frequency baseline components from the IPwaveform, and additionally generates a clear, well-defined zero-pointcrossing corresponding to each peak in the IP signal. Each peakcorresponds to each respiration event. The derivative used for thiscalculation is typically a 5-point derivative, meaning that data pointIPP_(xi+5) is subtracted from data point IPP_(xi) to calculate thederivative. When the IP waveform is sampled at 50 Hz, which ispreferred, this means data points separated by 0.1 seconds are used forthe derivative. Such a method for taking a derivative is preferred overthat using directly sequential data points, i.e. a derivative where datapoint IPP_(xi+1) is subtracted from data point IPP_(x) (i.e. the datapoints are separated by 0.02 seconds for data collection rates of 50Hz). A 5-point derivative typically yields a relatively strong signalfeaturing a high signal-to-noise ratio and minimal phase distortion.Additionally, as shown in FIG. 9, the passband of the digital filter hasa significant impact on the derivatized signal, and features an optimalvalue that is closely coupled to the actual value of RR. For example,for the signals shown in FIG. 9 (corresponding to roughly 8breaths/minute), the ideal high-frequency cutoff for the passband isnear 2.5 Hz, as indicated in the figure with a star. This yields asignal where the respiratory-induced peaks can be easily processed bycounting the zero-point crossing with a counting algorithm (step 186).Once determined, this initial guess at RR, along with the derivatizedsignal used to determine it, is compared to a series of pre-determinedmetrics to estimate the accuracy of the determination (step 188). Inparticular, the number of peaks determined during a pre-determined timeperiod (e.g. 20 seconds) is then compared over three consecutiveperiods. The standard deviation (SD in FIG. 8) of the counts withinthese periods is then calculated using standard means, and then comparedto a pre-determined value (3) to ensure that the RR is relativelyconstant during the measurement period. A low standard deviation, forexample, would indicate that the RR is relatively constant for the threeconsecutive 20-second periods, which in turns indicates that themeasurement is likely accurate. In contrast, a high standard deviationindicates that the RR is either changing rapidly or characterized by awaveform having a low signal-to-noise ratio, which in turn indicatesthat the measurement is likely inaccurate. In typical cases, β has avalue between 0-2 breaths/minute. The actual value of RR (ω in FIG. 8)is then compared to pre-determined threshold values (ε1 and ε2) toestimate if it is realistic. For example, ε1 represents an upper levelof RR, and is typically 60 breaths/minute. At this level a patient maybe hyperventilating. ε2 represents a lower level of RR, and is typicallyabout 5 breaths/minute. Below this and a patient may be undergoingrespiratory failure.

If the RR value calculated during steps 180-186 meets the criteriadefined in step 188, it is considered to be valid, or ‘true’ (T in FIG.8), and is reported by the body-worn monitor. In contrast, if the RRvalue fails the criteria defined in step 188, it is assumed to be‘false’ (F in FIG. 8), and further processing is performed. Inparticular, the raw IP waveform is processed again with a digitalbandpass filter characterized by a different passband (step 190) whichis typically 12 Hz. The second filter may yield a waveform featuringhigh-frequency components that are removed by the first filter. Afterthis filtering step the calculations originally performed during steps184, 186, 188 are repeated. FIG. 10 indicates the merits of processingthe raw IP waveform with a digital filter having an increase passband.In this case, the patient is undergoing a high RR (roughly 60breaths/minute), with each sharp pulse in the IP waveform composed of alarge and far-ranging collection of frequency components. Thus,filtering this waveform with the 2.5 Hz digital filter described in step182 and shown in the lower portion of FIG. 10 yields a filtered IPwaveform wherein the breathing-induced pulses are depleted. Taking thederivative of this signal yields the waveform shown in the lowerright-hand portion of FIG. 10. The waveform lacks the information toproperly determine RR. In contrast, as indicated in FIG. 10 by a star,digitally filtering the raw IP waveform with a 12 Hz passband yields arelatively noise-free signal from which a derivatized waveform can bedetermined as described above (step 192). From this waveform, zero-pointcrossings, each corresponding to an individual breath, can be easilycounted as used to estimate a value of RR (step 194). This value is thencompared to the same pre-determined values (β, ε1, ε2) used during step188 to estimate the validity of the calculated RR (step 196). Ifdetermined to be accurate, this value is reported by the body-wornmonitor as described above; if not, the algorithm determines that anaccurate measurement cannot be made, a series of dashes (e.g. ‘---’) arerendered by the monitor, and the process is repeated.

Algorithms 2 and 3—Adaptive Filtering

Both Algorithms 2 and 3 describe methods for determining RR from ACC andIP waveforms using a technique called ‘adaptive filtering’. The generalpremise of adaptive filtering, as used in this application, is thatmotion-induced frequency components in the ACC waveform are determinedand then filtered from the simultaneously measured IP waveform. Thisyields a clean, relatively uncorrupted waveform from which RR can beaccurately determined. In Algorithm 2, the coefficients for adaptivefiltering are determined from the ACC waveform by processing performedon the wrist-worn transceiver. This may be computationally ‘expensive’,and thus increase power consumption, but has the added benefit that allcalculations can be done in the absence of a remote server. In Algorithm3, both IP and ACC waveforms are transmitted to the remote serverfollowing a measurement, and the coefficients for adaptive filtering arecalculated on this platform. Afterwards, they are sent back to thewrist-worn transceiver, where the calculation is completed.Alternatively, the waveforms are fully processed on the remote serverwith adaptive filtering, and values of RR are transmitted back to thewrist-worn transceiver. In both cases, once received, the values of RRare displayed and availed for any follow-on alarming/alertingapplications.

FIG. 11 provides a high-level overview of Algorithm 2, which features awrist-worn transceiver 202 that collects ACC and IP waveforms, processesthe IP waveforms to determine that motion is present, and thencollectively processes the ACC and IP waveforms with an adaptive filteralgorithm to determine RR. Once calculated, this parameter is wirelesslytransmitted to a remote server 200 for storage and further processing.During Algorithm 3, shown schematically in FIG. 12, the wrist-worntransceiver 212 collects ACC and IP waveforms, and wirelessly transmitsthese to the remote server 210 for processing. The remote serverdetermines adaptive filter parameters as described in detail below, andthen wirelessly transmits these back to the transceiver 212, which usesthem to digitally filter the IP waveform to determine RR in the presenceof motion. Once determined, the value of RR is transmitted from thetransceiver 212 to the server 210 for storage and follow-on analysis.

According to Algorithm 3, the coefficients determined by the remoteserver 210 can be continuously used by the wrist-worn transceiver 212for an extended, follow-on period to adaptively filter IP waveforms.This further increases computational efficiency and reduces powerconsumption on the transceiver. Typically the follow-on period isseveral minutes, and preferably the motion during this period is similarin type and magnitude to that used when the coefficients were originallycalculated. This ensures that motion can be adequately filtered out fromthe IP waveform. If the type or magnitude of the patient's motionchanges, both IP and ACC waveforms are retransmitted from thetransceiver 212 to the remote server 210, and the process illustrated inFIG. 12 is repeated.

FIG. 13 describes a general algorithm for adaptive filtering which canbe used for both Algorithm 2 and/or 3. Both algorithms rely on a ‘batchprocess’ technique, which is designed for a linear deterministic system,and uses the ACC waveforms measured from the chest-worn accelerometer(component 12 in FIG. 1) as a reference signal. In alternateembodiments, this approach can be replaced with more sophisticatedmodels, such as those involving recursive methods or non-lineardeterministic systems. As described above, both Algorithms 2 and 3 beginwith a determination that the patient is moving (e.g. moving their armsor legs), but not walking (step 220). The body-worn monitor thenmeasures ACC and IP waveforms (step 222), and then the adaptive filtercoefficients are determined (step 224) on either the wrist-worntransceiver (Algorithm 2) or the remote server (Algorithm 3). Oncedetermined, the coefficients are used to adaptively filter the IPwaveform to remove any motion-induced noise (step 226), resulting in arelatively noise-free waveform that is uncorrupted by motion and othernoise (e.g. that from electrical and mechanical sources). At this pointthe waveform is processed in a manner similar to that described withreference to Algorithm 1. Specifically, the waveform is derivatized andsquared (step 228) to remove any low-frequency baseline components andgenerate a zero-point crossing for each respiratory-induced pulse. Thealgorithm then counts the zero-point crossings (step 230) to determinean initial RR, which is then compared to the pre-determined values (β,ε1, ε2) described above to estimate if this rate is valid.

FIG. 14 highlights an algorithm 249 for determining the adaptivefiltering coefficients, and for performing an adaptive digital filter.Prior to implementing the algorithm, IP and ACC waveforms from the x, y,and z-axes are collected at a frequency of 50 Hz for a period rangingfrom about 2-3 minutes, resulting in N samples as defined below. Duringthis period it is assumed that the patient is undergoing moderatemotion, and that the IP waveform contains a motion artifact.

The noise reference u[i] is defined as the vector length of the chestacceleration, as determined with the accelerometer mounted on thepatient's chest, which combines all three axes into a single parameteras given in Eq. 1 below (step 250).

u[i]=√{square root over((acc_(Cx)[i])²+(acc_(Cy)[i])²+(acc_(Cz)[i])²)}  (1)

The measured output y[i] is the IP waveform, which contains signals dueto both respiration and motion. Note that for this calculation the twowaveforms should be sampled at the same rate. If the IP waveform issampled at a higher frequency than that used for the ACC waveform (e.g.50 Hz), then this waveform must be converted into a 50 Hz waveform usingconventional mathematical techniques, such as decimation or a numerical‘mapping’ operation.

At this point zero mean input and output signals for u[i] and y[i] areconstructed by subtracting the ensemble signal mean from each signal.This operation, shown below in Eqs. 2 and 3, effectively removes any DCoffset (step 252).

$\begin{matrix}{{u\lbrack i\rbrack} = {{u\lbrack i\rbrack} - {\left( \frac{1}{N} \right){\sum\limits_{k = 1}^{N}{u\lbrack k\rbrack}}}}} & (2) \\{{y\lbrack i\rbrack} = {{y\lbrack i\rbrack} - {\left( \frac{1}{N} \right){\sum\limits_{k = 1}^{N}{y\lbrack k\rbrack}}}}} & (3)\end{matrix}$

A mathematical model used to define the impulse response function of theadaptive filter must be chosen, with the two primary options beingfilters based on FIR (finite impulse response) or ARMA (autoregressivemoving average) models. Both models are indicated below in Eqs. 4 and 5,but only one should be chosen and implemented during the algorithm (step254):

$\begin{matrix}{{FIR}\mspace{14mu} {Model}} & \; \\{{H\lbrack z\rbrack} = {b_{0} + {b_{1}z^{- 1}} + \ldots + {b_{L}z^{- L}}}} & (4) \\{{ARMA}\mspace{14mu} {Model}} & \; \\{{H\lbrack z\rbrack} = \frac{b_{0} + {b_{1}z^{- 1}} + \ldots + {b_{m}z^{- m}}}{1 + {a_{1}z^{- 1}} + \ldots + {a_{n}z^{- p}}}} & (5)\end{matrix}$

At this point the order of the filter is determined. For the FIR model,the order is L; for the ARMA model, the order is m and p. In a preferredembodiment, the orders for L, m, and p are, respectively, 25, 2, and 2;these values may vary depending on the degree of motion and thecomputational capabilities on the wrist-worn transceiver. A columnvector phi φ[i] is then formulated for each time-sampled data point forboth the FIR and ARMA models, as described in Eqs. 6 and 7 below (step256). In these equations the superscript T represents the matrixtranspose.

FIR Model

φ[i]=[u[i]u[i−1]u[i−2]u[i−3] . . . u[i−25]]^(T)  (6)

ARMA Model

φ[i]=[u[i]u[i−1]u[i−2]−y[i−1]−y[i−2]]^(T)  (7)

The square matrix R_(N) is then constructed using the column vectorsdefined above, as shown in Eqs. 8 and 9 (step 258):

$\begin{matrix}{{FIR}\mspace{14mu} {Model}} & \; \\{R_{N} = {\sum\limits_{i = {L + 1}}^{N}{{\phi \lbrack i\rbrack}{\phi^{T}\lbrack i\rbrack}}}} & (8) \\{{ARMA}\mspace{14mu} {Model}} & \; \\{R_{N} = {\sum\limits_{i = {p + 1}}^{N}{{\phi \lbrack i\rbrack}{\phi^{T}\lbrack i\rbrack}}}} & (9)\end{matrix}$

The column vector B is then defined from the measured output, y[i] andcolumn vector, φ[i], as defined below in Eqs. 10 and 11 (step 260):

$\begin{matrix}{{FIR}\mspace{14mu} {Model}} & \; \\{B = {\sum\limits_{i = {L + 1}}^{N}{{y\lbrack i\rbrack}{\phi \lbrack i\rbrack}}}} & (10) \\{{ARMA}\mspace{14mu} {Model}} & \; \\{B = {\sum\limits_{i = {p + 1}}^{N}{{y\lbrack i\rbrack}{\phi \lbrack i\rbrack}}}} & (11)\end{matrix}$

At this point the coefficients of the FIR and ARMA models can be writtenas a column vector, θ, as given below in Eqs. 12 and 13:

FIR Model

θ=[b ₀ b ₁ b ₂ . . . b _(L)]^(T)  (12)

ARMA Model

θ=[b ₀ b ₁ b ₂ a ₁ a ₂]^(T)  (13)

The square matrix and two column vectors obey the relationship givenbelow in Eq. 14 for the adaptive filtering process.

R _(N) θ=B  (14)

The adaptive filtering coefficients for each model can be identifiedfrom the data using the expression below in Eq. 15, where the accentedcolumn vector {circumflex over (θ)} represents the identifiedcoefficients (step 262):

{circumflex over (θ)}=R _(N) ⁻¹ B  (15)

Once identified, the filter coefficients can be collectively processedwith the IP waveform and used to remove any motion artifacts, leavingonly the respiratory component of the signal y_(R)[i], as shown below inEq. 16 (step 264):

y _(R)[i]=y[i]−{circumflex over (θ)}φ[i]  (16)

As described above, determination of the filter coefficients can be doneon either the wrist-worn transceiver (Algorithm 2) or on the remoteserver (Algorithm 3). Once determined, the coefficients can be used torecover RR in real-time using the algorithm described above. Preferablythe filter coefficients are updated when analysis of the ACC waveformindicates that the patient's motion has changed. Such a change can berepresented by a change in the magnitude of the motion, a change inposture, or a change in activity state. Alternatively, the filtercoefficients can be updated on a periodic basis, e.g. every 5 minutes.

There may be a time difference or delay between motion signals in theACC waveform, and motion artifacts in the IP waveform. Such a delay, forexample, may be due to real phenomena (e.g. a physiological effect) oran artifact associated with the electrical systems that measure therespective waveforms (e.g. a phase delay associated with an amplifier oranalog filter that processes these waveforms). In any event, thealgorithm should compensate for the delay before performing calculationsshown in Eqs. 1-16, above. Such compensation can be performed using asimple time-domain analysis of ACC and IP signals influenced by awell-defined motion (e.g. a step). Alternatively, the compensation canbe done during manufacturing using a one-time calibration procedure.

FIGS. 15, 16, and 17 illustrate how the above-described adaptivefiltering algorithm can be applied to both ACC and IP waveforms. In eachof the figures, the graphs show the ACC waveform filtered with aninitial, non-adaptive filter (15A, 16A, 17A; 0.01→2 Hz bandpass), andthe IP waveform filtered under similar conditions with a slightly largerbandpass filter (15B, 16B, 17B; 0.01→12 Hz bandpass). Typically the IPwaveform is filtered with the larger bandpass so that high-frequencycomponents composing the rising and falling edges of pulses within thesewaveforms are preserved.

Once filtered, the IP waveform is processed as described above todetermine an initial RR. This value may include artifacts due to motion,electrical, and mechanical noise that erroneously increases or decreasesthe initial RR value. But typically such errors have little impact onthe final RR value that results from the adaptive filter. The middlegraph (FIGS. 15C, 16C, and 17C) in each figure show the IP waveformprocessed with the adaptive filter. In all cases this waveform featuresan improved signal-to-noise ratio compared to data shown in the topgraph (15A, 16A, 17A), which is processed with a non-adaptive (andrelatively wide) filter. Typically the narrow bandpass on the adaptivefilter removes many high-frequency components that contribute the sharprising and falling edges of pulses in the ACC waveforms. This slightlydistorts the waveforms by rounding the pulses, giving the filteredwaveform a shape that resembles a conventional sinusoid. Suchdistortion, however, has basically no affect on the absolute number ofpulses in each waveform which are counted to determine RR.

The adaptively filtered IP waveform is then derivatized and graphed inFIGS. 15D, 16D, and 17D. This waveform is then processed with theabove-mentioned signal processing techniques, e.g. squaring thederivative and filtering out lobes that fall beneath pre-determinedthreshold values, to yield an algorithm-determined ‘count’, indicated inFIGS. 15E, 16E, and 17E as a series of black triangles. The count isplotted along with the adaptively filtered IP waveforms from FIGS. 15C,16C, and 17C. Exact overlap between each pulse in the waveform and thecorresponding count indicates the algorithm is working properly. Datafrom each of the figures correspond to varying respiratory behavior (5,17, and 38 breaths/minute in, respectively, FIGS. 15, 16, and 17), andindicate that this technique is effective over a wide range of breathingfrequencies. The right-hand side of the figures (FIGS. 15F, 16F, and17F) show a series of steps 290-294 that indicate the analysis requiredto generate the corresponding graphs in the figure.

Algorithm 4—Power Spectra Analysis

FIG. 18 shows a high-level overview of Algorithm 4, which is typicallyused to remove motion-related artifacts having relatively largemagnitudes, such as those associated with running and walking, from theIP waveform. Algorithm 4 deconstructs both time-domain ACC and IPwaveforms into their frequency-domain components, and then collectivelyprocesses these components to remove artifacts due to motion. Typicallythis algorithm involves collecting the two waveforms on the wrist-worntransceiver 302, and then processing them with the algorithms describedabove to determine if motion is present. If it is, the waveforms arewirelessly transmitted to the remote server, where they are processedwith the algorithm described below to determine and then collectivelyprocess their frequency spectra to remove the affects of motion. RR isdetermined on the server, where it is stored, further processed, andfinally sent to the wrist-worn transceiver 302 for purposed related todisplay and alarming.

FIG. 19 shows the computational details of Algorithm 4. The algorithmbegins by determining if the patient is walking or running (step 320).This is done by processing ACC waveforms according to the techniquesdescribed in the above-described patent application, the contents ofwhich are incorporated herein by reference. Once the patient's walkingor running state is identified, the ACC and IP waveforms (represented inEq. 17 by a(t)) are wirelessly transmitted to the remote server, whichthen determines their frequency-domain power spectra A(ω), as defined byEq. 17 (step 322):

$\begin{matrix}{{A(\omega)} = \left\lbrack {\frac{1}{\sqrt{2\pi}}{\int_{- \infty}^{\infty}{{a(t)}e^{{- i}\; \omega \; t}\ {dt}}}} \right\rbrack^{2}} & (17)\end{matrix}$

A(ω) shown above in Eq. 17 represents a power spectra determined from acontinuous Fourier Transform. For discrete waveforms featuring an arrayof discrete values a_(n), like the ones measured with the body-wornmonitor, A(ω) can be rewritten as:

$\begin{matrix}{{A(\omega)} = \left\lbrack {\frac{1}{\sqrt{2\pi}}{\int_{n = {- \infty}}^{n = {+ \infty}}{a_{n}e^{{- i}\; \omega \; t}\ {dt}}}} \right\rbrack^{2}} & (18)\end{matrix}$

or alternatively as:

$\begin{matrix}{{A(\omega)} = \frac{{F(\omega)}{F^{*}(\omega)}}{2\pi}} & (19)\end{matrix}$

Where F(ω) is the discrete Fourier Transform and F*(ω) is its complexconjugate. Power spectra determined this way for both IP and ACCwaveforms are shown, for example, in FIGS. 20C, 20D, 21C, and 21D.

The power spectra of the ACC waveform is then processed to determine acollection of frequencies corresponding to the patient's motion, whichas described above corresponds to walking or running for this particularalgorithm (step 324). Typically these motions are characterized by acollection of frequencies that are peaked between 0.5 and 2.0 Hz. Oncethe peak frequency is identified, a notch filter having a top-hatprofile and a bandwidth of 0.5 Hz is constructed to surround it.Typically the primary walking frequency is positioned at the center ofthe notch filter, which extends 0.25 Hz on both the positive andnegative ends. The notch filter is then used to process the powerspectra of the IP waveform by only passing components outside of thenotch (step 326). The resulting spectra of the IP waveform will now lackthe frequency components related to motion. To simplify determination ofthe central respiratory signal, the IP waveform is then processed with asmoothing filter typically having a bandwidth of 4 Hz (step 328).Alternatively the spectrum can be processed with a rolling average. Bothtechniques smooth out discrete frequency components, creating acontinuous set of peaks which can then be analyzed with conventionalpeak-finding algorithms (step 330). Examples of peak-finding algorithmsinclude those that find the maximum frequency of each peak in thespectrum.

The frequency of the dominant peak in the filtered IP spectrumcorresponds to RR. Once determined, this value and the spectrum it isextracted from can be compared to a series of pre-determined metrics toestimate the accuracy of the resulting RR (step 332). For example, thepower (i.e. magnitude or density) of the peak can be compared to themetric a to determine if it is above a noise floor and corresponds to alevel describing an actual RR. Additionally, the RR is compared to theε1 and ε2 metrics described above to determine if the rate is within theboundaries (typically 5-60 breaths/minute) of human respiration. If so,RR is stored, further processed, and sent to the wrist-worn transceiverfor display and further processing. If not, a state of ‘no measurement’(indicated by dashes ‘---’) is recorded, and the process is thenrepeated.

FIGS. 20 and 21 show how Algorithm 4 can effectively determine RR for apatient that is running (FIG. 20) and walking (FIG. 21). Time-domain IPand ACC waveforms for the patient, collected by the body-worn monitor,are shown respectively in FIGS. 20A and 20B. In the IP waveform, theslowly varying pulses occurring approximately every 5 seconds correspondto individual breaths, while the sharp peaks in both the ACC and IPwaveforms that occur roughly twice each second corresponds to runningsteps. In this case, motion artifacts from the running motion clearlycouple into the IP waveform.

FIGS. 20C and 20D show, respectively, frequency-domain power spectra ofboth the IP and ACC waveforms. Clearly shown in the power spectra of theIP waveform is a dominant peak near 2.7 Hz corresponding to motionartifacts from the patient's running. A much weaker peak is also evidentnear 0.2 Hz. The power spectra corresponding to the ACC waveformfeatures only a peak near 2.7 Hz that includes nearly the exact samefrequency components as the corresponding peak in the IP waveform. Asshown in FIG. 20D, a top-hat shaped notch filter centered at 2.7 Hz witha bandwidth of 0.5 Hz, indicated by the gray area 380, is then appliedto the IP waveform. This yields the filtered waveform, shown in FIG.20E, that lacks the high-frequency peak associated with the motionartifacts, as shown by the gray area 382. The relatively low-frequencypeak near 0.2 Hz is now dominant. Further processing of this spectrumwith a 4 Hz smoothing function eliminates most of the jagged edgespresent in FIG. 20E, yielding the continuous power spectrum shown inFIG. 20F. Processing this spectrum with a simple peak-finding algorithmyields the patient's actual RR, which corresponds to about 13breaths/minute.

FIG. 21 shows a case where the RR and the motion artifact, in this casecaused by walking, are relatively close in frequency. Here, thetime-domain IP waveform shown in FIG. 21A lacks a strong, periodicrespiratory signal, and is strongly corrupted by the walking-inducedsignals in the ACC waveform, shown in FIG. 21B. Each pulse in the ACCwaveform corresponds to a unique step. The corresponding power spectra,shown in FIGS. 21C and 21D, show frequency components corresponding toboth the motion artifact (near 0.75 Hz) and the respiratory signal (near0.25 Hz). Because the respiratory signal lacks a clear, well-definedbreathing pattern, the corresponding power spectrum features a range afrequencies between 0.2 and 0.5 Hz.

Application of the 0.5 Hz bandwidth notch filter, shown by the gray area380 in FIG. 21D, removes motion artifacts from the IP waveform, leavingonly a range of frequencies between 0.2 and 0.5 Hz. This portion of thespectrum features a large number of sharp peaks which are difficult toisolate with conventional peak-finding algorithms. However applicationof the 4 Hz smoothing function, as indicated by FIG. 21D, reduces thiscollection of frequencies to a single, broad peak shown in FIG. 21E thatcorresponds to the patient breathing at about 18 breaths/minute.

As shown in FIGS. 22-24, when the patient is stationary, ACC waveformsmeasured from the patient's chest (or, alternatively, belly) aresensitive to RR. This is because slight, breathing-induced motions ofthe patient's torso can be detected by the accelerometer. These figuresshow the time-domain IP waveforms (FIGS. 22A, 23A, 24A) and ACCwaveforms (FIGS. 22B, 23B, 24B), both of which show pulses, lined upexactly in time, that correspond to individual breaths. Thefrequency-domain power spectrum (FIGS. 22C, 23C, 24C) show how thefrequency components of these time-domain waveform are roughlyequivalent, indicating both waveforms are sensitive to RR. Thisequivalency holds for normal breathing when the patient is on their back(FIG. 22); fast, shallow breathing when the patient is on their back(FIG. 23); and normal breathing when the patient is lying face down(FIG. 24). In all these cases both the ACC and IP waveforms can beprocessed collectively or independently to estimate RR. For collectiveprocessing, RRs may be determined from both waveforms, and then averagedtogether to generate a single value. Or a value may be determined fromeach waveform, and then subjected to a series of metrics (like thosedescribed above using α, β, ε1, ε2) to determine if one or both of thecalculated RRs is valid. Processing of the waveforms may involve simplepulse-counting algorithms that analyze time-domain waveforms, like theones shown in FIGS. 22A, 22B, 23A, 23B, 24A, 24B, or more sophisticatedspectral-analysis algorithms that analyze frequency-domain waveforms,like the ones shown in FIGS. 22C, 23C, 24C.

FIGS. 25 and 26 show how a filtering algorithm (FIG. 25) and rollingaverage (FIG. 26), when applied to a conventional power spectrum,facilitates determination of RR. The power spectrum in FIG. 25A (shownin the figure's top portion) is unprocessed, and features a collectionof peaks positioned in three groupings near 0.3, 0.6, and 0.7 Hz. Asdescribed above, a low-pass smoothing filter based on a FIR functionsmoothes out these sharply varying features, resulting in a continuousspectrum that is relatively easy to analyze to determine a singlefrequency corresponding to RR. In this case, the smoothing filter isprocessing the power spectrum as if it were a time-domain waveform. FIG.25B, for example, shows the spectrum after processing with a 5 Hzlow-pass filter. Here, the individual peaks are mostly smoothed out,resulting in three primary peaks related to RR. Note that these peaksare not necessarily artifacts, and instead are due to physiologicalvariations in the patient's breathing pattern. Progressively decreasingthe filter cutoff removes more and more of the features that result insharply varying peaks in the frequency spectrum. Ultimately when a 0.5Hz filter is used, this results in a single, broad peak that lacks thedefinition to accurately determine RR. As described with reference toFIGS. 20F and 21F, the ideal smoothing filter has a cutoff of about 4Hz.

FIG. 26 shows an alternate embodiment of the invention wherein a rollingaverage is used in place of the smoothing filter described above withreference to FIG. 25. Here, the rolling average is gradually increasedfrom 0 points (FIG. 26A, at the top of the page) to 100 points. When theaverage is increased above about 20 points the features in the frequencyspectrum begin to blend together to form a well-defined peak that can beeasily analyzed using the peak-finding algorithm referenced above.

Referring to FIGS. 27 and 28, the number of time-domain samples used tocalculate an associated Fourier Transform and power spectrum will have asignificant influence on the resolution of the power spectrum. Ingeneral, increasing the number of time-domain samples will increaseresolution in the power spectrum. According to this invention, thefrequency-domain resolution can be arbitrarily increased by addingconstant values to the time-domain ACC and IP waveform. In thistechnique, typically called ‘zero padding’, the constant values aretypically 0, or alternatively a constant value.

Both FIGS. 27 and 28 show a time-domain IP waveform (FIGS. 27A and 28A,shown at the top of the page) along with associated power spectracalculated, respectively, using 1000 points (FIGS. 27B, 28B), 500 points(FIG. 27C, 28C), and 250 points (FIG. 27D, 28D). The IP waveform forthis figure was sampled at 25 Hz, and thus the time-domain markers thatcorrespond to the above-mentioned points are indicated by a light graycircle (1000 points), a dark gray circle (500 points), and a blackcircle (250 points). As shown in FIGS. 27B, 28B, 27C, 28C, power spectradetermined with both 1000-point and 500-point Fourier Transforms featureroughly the same frequency profile, with, as expected, the 1000-pointspectra showing relatively greater resolution. Power spectra calculatedfrom the 250-point Fourier Transform show one or more very broad peaks,and, importantly, undergo a frequency shift which trends toward higherfrequencies for the dominant peak. Such a frequency shift will result inan erroneous value for the eventual RR. For this reason, it is preferredthat the Fourier Transform calculation use a minimum of 500 points,which corresponds to 10 seconds of data sampled at 50 Hz for thetime-domain ACC and IP waveforms. The ideal number of points for thiscalculation is about 750, which at 50 Hz can be achieved in 15 seconds.Additionally, the frequency-domain data shown in FIGS. 27 and 28 wascalculated with substantially fewer time-domain samples than that usedfor all previous figures (e.g. FIGS. 20, 21; Fourier Transforms forthese figures were calculated with approximately 5000 points). Thisresults in a relatively low-resolution power spectrum that may not needthe additional 4 Hz smoothing filter, indicated above with reference toFIGS. 20F and 21F. Here, the smoothing filter is essentiallyartificially applied due to the relatively low resolution of the powerspectra.

Processing ACC Waveforms to Determine Posture

In addition to activity level, as described above and indicated in FIGS.21-24, a patient's posture can influence how the above-described systemgenerates alarms/alerts from RR, cNIBP, and other vital signs. Forexample, the alarms/alerts related to both RR and cNIBP may varydepending on whether the patient is lying down or standing up. FIG. 29indicates how the body-worn monitor can determine motion-relatedparameters (e.g. degree of motion, posture, and activity level) from apatient 410 using time-dependent ACC waveforms continuously generatedfrom the three accelerometers 412, 413, 414 worn, respectively, on thepatient's chest, bicep, and wrist. The height of the patient's arm canaffect the cNIBP measurement, as blood pressure can vary significantlydue to hydrostatic forces induced by changes in arm height. Moreover,this phenomenon can be detected and exploited to calibrate the cNIBPmeasurement, as described in detail in the above-referenced patentapplications, the contents of which have been previously incorporated byreference. As described in these documents, arm height can be determinedusing DC signals from the accelerometers 413, 414 disposed,respectively, on the patient's bicep and wrist. Posture, in contrast,can be exclusively determined by the accelerometer 412 worn on thepatient's chest. An algorithm operating on the wrist-worn transceiverextracts DC values from waveforms measured from this accelerometer andprocesses them with an algorithm described below to determine posture.

Specifically, torso posture is determined for a patient 410 using anglesdetermined between the measured gravitational vector and the axes of atorso coordinate space 411. The axes of this space 411 are defined in athree-dimensional Euclidean space where

_(CV) is the vertical axis,

_(CH) is the horizontal axis, and

_(CN) is the normal axis. These axes must be identified relative to a‘chest accelerometer coordinate space’ before the patient's posture canbe determined.

The first step in determining a patient's posture is to identifyalignment of

_(CV) in the chest accelerometer coordinate space. This can bedetermined in either of two approaches. In the first approach,

_(CV) is assumed based on a typical alignment of the body-worn monitorrelative to the patient. During a manufacturing process, theseparameters are then preprogrammed into firmware operating on thewrist-worn transceiver. In this procedure it is assumed thataccelerometers within the body-worn monitor are applied to each patientwith essentially the same configuration. In the second approach,

_(CV) is identified on a patient-specific basis. Here, an algorithmoperating on the wrist-worn transceiver prompts the patient (using,e.g., video instruction operating on the wrist-worn transceiver, oraudio instructions transmitted through a speaker) to assume a knownposition with respect to gravity (e.g., standing upright with armspointed straight down). The algorithm then calculates

_(CV) from DC values corresponding to the x, y, and z axes of the chestaccelerometer while the patient is in this position. This case, however,still requires knowledge of which arm (left or right) the monitor isworn on, as the chest accelerometer coordinate space can be rotated by180 degrees depending on this orientation. A medical professionalapplying the monitor can enter this information using the GUI, describedabove. This potential for dual-arm attachment requires a set of twopre-determined vertical and normal vectors which are interchangeabledepending on the monitor's location. Instead of manually entering thisinformation, the arm on which the monitor is worn can be easilydetermined following attachment using measured values from the chestaccelerometer values, with the assumption that

_(CV) is not orthogonal to the gravity vector.

The second step in the procedure is to identify the alignment of

_(CN) in the chest accelerometer coordinate space. The monitordetermines this vector in the same way it determines

_(CV) using one of two approaches. In the first approach the monitorassumes a typical alignment of the chest-worn accelerometer on thepatient. In the second approach, the alignment is identified byprompting the patient to assume a known position with respect togravity. The monitor then calculates

_(CN) from the DC values of the time-dependent ACC waveform.

The third step in the procedure is to identify the alignment of

_(CH) in the chest accelerometer coordinate space. This vector istypically determined from the vector cross product of

_(CV) and

_(CN), or it can be assumed based on the typical alignment of theaccelerometer on the patient, as described above.

A patient's posture is determined using the coordinate system describedabove and in FIG. 29, along with a gravitational vector

_(G) that extends normal from the patient's chest. The angle between

_(CV) and

_(G) is given by Eq. 20:

$\begin{matrix}{{\theta_{VG}\lbrack n\rbrack} = {\arccos \left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{CV}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{CV}}} \right)}} & (20)\end{matrix}$

where the dot product of the two vectors is defined as:

_(G)[n]·

_(CV)=(y _(Cx)[n]×r _(CVx))+(y _(Cy)[n]×r _(CVy))+(y _(Cz)[n]×r_(CVz))  (21)

The definition of the norms of

_(G) and

_(CV) are given by Eqs. 22 and 23:

∥

_(G)[n]∥=√{square root over ((y _(Cx)[n])²+(y _(Cy)[n])²+(y_(Cz)[n])²)}  (22)

∥

_(CV)∥=√{square root over ((r _(CVx))²+(r _(CVy))²+(r _(CVz))²)}  (23)

As indicated in Eq. 24, the monitor compares the vertical angle θ_(VG)to a threshold angle to determine whether the patient is vertical (i.e.standing upright) or lying down:

if θ_(VG)≤45° then Torso State=0, the patient is upright  (24)

If the condition in Eq. 24 is met the patient is assumed to be upright,and their torso state, which is a numerical value equated to thepatient's posture, is equal to 0. The patient is assumed to be lyingdown if the condition in equation (5) is not met, i.e. θ_(VG)>45degrees. Their lying position is then determined from angles separatingthe two remaining vectors, as defined below.

The angle θ_(NG) between

_(CN) and

_(G) determines if the patient is lying in the supine position (chestup), prone position (chest down), or on their side. Based on either anassumed orientation or a patient-specific calibration procedure, asdescribed above, the alignment of

_(CN) is given by Eq. 25, where i, j, k represent the unit vectors ofthe x, y, and z axes of the chest accelerometer coordinate spacerespectively:

_(CN) =r _(CNx) î+r _(CNy) ĵ+r _(CNz) {circumflex over (k)}  (25)

The angle between

_(CN) and

_(G) determined from DC values extracted from the chest ACC waveform isgiven by Eq. 26:

$\begin{matrix}{{\theta_{NG}\lbrack n\rbrack} = {\arccos \left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{CN}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{CN}}} \right)}} & (26)\end{matrix}$

The body-worn monitor determines the normal angle θ_(NG) and thencompares it to a set of predetermined threshold angles to determinewhich position in which the patient is lying, as shown in Eq. 27:

if θ_(NG)≤35° then Torso State=1, the patient is supine

if θ_(NG)≥135° then Torso State=2, the patient is prone  (27)

If the conditions in Eq. 27 are not met then the patient is assumed tobe lying on their side. Whether they are lying on their right or leftside is determined from the angle calculated between the horizontaltorso vector and measured gravitational vectors, as described above.

The alignment of

_(CH) is determined using either an assumed orientation, or from thevector cross-product of

_(CV) and

_(CN) as given by Eq. 28, where i, j, k represent the unit vectors ofthe x, y, and z axes of the accelerometer coordinate space respectively.Note that the orientation of the calculated vector is dependent on theorder of the vectors in the operation. The order below defines thehorizontal axis as positive towards the right side of the patient'sbody.

_(CH) =r _(CVx) î+r _(CVy) ĵ+r _(CVz) {circumflex over (k)}=

_(CV)×

_(CN)  (28)

The angle θ_(HG) between

_(CH) and

_(G) is determined using Eq. 29:

$\begin{matrix}{{\theta_{HG}\lbrack n\rbrack} = {\arccos \left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{CH}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{CH}}} \right)}} & (29)\end{matrix}$

The monitor compares this angle to a set of predetermined thresholdangles to determine if the patient is lying on their right or left side,as given by Eq. 30:

if θ_(HG)≥90° then Torso State=3, the patient is on their right side

if θ_(NG)<90° then Torso State=4, the patient is on their leftside  (30)

Table 1 describes each of the above-described postures, along with acorresponding numerical torso state used to render, e.g., a particularicon on a remote computer:

TABLE 1 postures and their corresponding torso states Posture TorsoState standing upright 0 supine: lying on back 1 prone: lying on chest 2lying on right side 3 lying on left side 4 undetermined posture 5

FIGS. 30A and 30B show, respectively, graphs of time-dependent ACCwaveforms measured along the x, y, and z-axes (FIG. 30A), and the torsostates (i.e. postures; FIG. 30B) determined from these waveforms for amoving patient, as described above. As the patient moves, the DC valuesof the ACC waveforms measured by the chest accelerometer varyaccordingly, as shown in FIG. 30A. The body-worn monitor processes thesevalues as described above to continually determine

_(G) and the various quantized torso states for the patient, as shown inFIG. 30B. The torso states yield the patient's posture as defined inTable 1. For this study the patient rapidly alternated between standing,lying on their back, chest, right side, and left side within a timeperiod of about 160 seconds. As described above, different alarm/alertconditions (e.g. threshold values) for vital signs can be assigned toeach of these postures, or the specific posture itself may result in analarm/alert. Additionally, the time-dependent properties of the graphcan be analyzed (e.g. changes in the torso states can be counted) todetermine, for example, how often the patient moves in their hospitalbed. This number can then be equated to various metrics, such as a ‘bedsore index’ indicating a patient that is so stationary in their bed thatlesions may result. Such a state could then be used to trigger analarm/alert to the supervising medical professional.

Hardware for Measuring RR

FIGS. 4A and 4B show how the body-worn monitor 100 described aboveattaches to a patient 70 to measure RR, cNIBP, and other vital signs.These figures show two configurations of the system: FIG. 4A shows thesystem used during the indexing portion of the Composite Technique, andincludes a pneumatic, cuff-based system 85, while FIG. 4B shows thesystem used for subsequent RR and cNIBP measurements. The indexingmeasurement typically takes about 60 seconds, and is typically performedonce every 4 hours. Once the indexing measurement is complete thecuff-based system 85 is typically removed from the patient. Theremainder of the time the monitor 100 performs the RR, HR, SpO2 andcNIBP measurements.

The body-worn monitor 100 features a wrist-worn transceiver 72,described in more detail in FIG. 5, featuring a touch panel interface 73that displays RR and other vital signs. A wrist strap 90 affixes thetransceiver 72 to the patient's wrist like a conventional wristwatch. Aflexible cable 92 connects the transceiver 72 to an optical sensor 94that wraps around the base of the patient's thumb. During a measurement,the optical sensor 94 generates a time-dependent PPG waveform which isprocessed along with an ECG to measure cNIBP, SpO2, and, in someapplications, RR. To determine ACC waveforms the body-worn monitor 100features three separate accelerometers located at different portions onthe patient's arm and chest. The first accelerometer is surface-mountedon a circuit board in the wrist-worn transceiver 72 and measures signalsassociated with movement of the patient's wrist. As described above,this motion can also be indicative of that originating from thepatient's fingers, which will affect the SpO2 measurement. The secondaccelerometer is included in a small bulkhead portion 96 included alongthe span of the cable 82. During a measurement, a small piece ofdisposable tape, similar in size to a conventional bandaid, affixes thebulkhead portion 96 to the patient's arm. In this way the bulkheadportion 96 serves two purposes: 1) it measures a time-dependent ACCwaveform from the mid-portion of the patient's arm, thereby allowingtheir posture and arm height to be determined as described in detailabove; and 2) it secures the cable 82 to the patient's arm to increasecomfort and performance of the body-worn monitor 100, particularly whenthe patient is ambulatory. The third accelerometer is mounted in thesensor module 74 that connects through cables 80 a-c to ECG electrodes78 a-c. As described in detail above, this accelerometer, which can alsobe mounted closer to the patient's belly, measures respiration-inducedmotion of the patient's chest and belly. These signals are thendigitized, transmitted through the cable 82 to the wrist-worntransceiver 72, where they are processed with an algorithm as describedabove to determine RR.

The cuff-based module 85 features a pneumatic system 76 that includes apump, valve, pressure fittings, pressure sensor, manifold,analog-to-digital converter, microcontroller, and rechargeable Li:ionbattery. During an indexing measurement, the pneumatic system 76inflates a disposable cuff 84 and performs two measurements according tothe Composite Technique: 1) it performs an inflation-based measurementof oscillometry to determine values for SYS, DIA, and MAP; and 2) itdetermines a patient-specific relationship between PTT and MAP. Thesemeasurements are described in detail in the above-referenced patentapplications entitled: ‘VITAL SIGN MONITOR FOR MEASURING BLOOD PRESSUREUSING OPTICAL, ELECTRICAL, AND PRESSURE WAVEFORMS’ (U.S. Ser. No.12/138,194; filed Jun. 12, 2008) and ‘BODY-WORN SYSTEM FOR MEASURINGCONTINUOUS NON-INVASIVE BLOOD PRESSURE (cNIBP)’ (U.S. Ser. No.12/650,354; filed Nov. 15, 2009), the contents of which have beenpreviously incorporated herein by reference.

The cuff 84 within the cuff-based pneumatic system 85 is typicallydisposable and features an internal, airtight bladder that wraps aroundthe patient's bicep to deliver a uniform pressure field. During theindexing measurement, pressure values are digitized by the internalanalog-to-digital converter, and sent through a cable 86 according to aCAN protocol, along with SYS, DIA, and MAP blood pressures, to thewrist-worn transceiver 72 for processing as described above. Once thecuff-based measurement is complete, the cuff-based module 85 is removedfrom the patient's arm and the cable 86 is disconnected from thewrist-worn transceiver 72. cNIBP is then determined using PTT, asdescribed in detail above.

To determine an ECG, the body-worn monitor 100 features a small-scale,three-lead ECG circuit integrated directly into the sensor module 74that terminates an ECG cable 82. The ECG circuit features an integratedcircuit that collects electrical signals from three chest-worn ECGelectrodes 78 a-c connected through cables 80 a-c. As described above,the ECG electrodes 78 a-c are typically disposed in a conventionalEinthoven's Triangle configuration, which is a triangle-like orientationof the electrodes 78 a-c on the patient's chest that features threeunique ECG vectors. From these electrical signals the ECG circuitdetermines up to three ECG waveforms, which are digitized using ananalog-to-digital converter mounted proximal to the ECG circuit, andsent through the cable 82 to the wrist-worn transceiver 72 according tothe CAN protocol. There, the ECG and PPG waveforms are processed todetermine the patient's blood pressure. Heart rate and RR are determineddirectly from the ECG waveform using known algorithms, such as thosedescribed above. More sophisticated ECG circuits (e.g. five andtwelve-lead systems) can plug into the wrist-worn transceiver to replacethe three-lead system shown in FIGS. 4A and 4B.

FIG. 5 shows a close-up view of the wrist-worn transceiver 72. Asdescribed above, it attaches to the patient's wrist using a flexiblestrap 90 which threads through two D-ring openings in a plastic housing106. The transceiver 72 features a touchpanel display 120 that renders aGUI 73 which is altered depending on the viewer (typically the patientor a medical professional). Specifically, the transceiver 72 includes asmall-scale infrared barcode scanner 102 that, during use, can scan abarcode worn on a badge of a medical professional. The barcode indicatesto the transceiver's software that, for example, a nurse or doctor isviewing the user interface. In response, the GUI 73 displays vital signdata and other medical diagnostic information appropriate for medicalprofessionals. Using this GUI 73, the nurse or doctor, for example, canview the vital sign information, set alarm parameters, and enterinformation about the patient (e.g. their demographic information,medication, or medical condition). The nurse can press a button on theGUI 73 indicating that these operations are complete. At this point, thedisplay 120 renders an interface that is more appropriate to thepatient, such as time of day and battery power.

The transceiver 72 features three CAN connectors 104 a-c on the side ofits upper portion, each which supports the CAN protocol and wiringschematics, and relays digitized data to the internal CPU. Digitalsignals that pass through the CAN connectors include a header thatindicates the specific signal (e.g. ECG, ACC, or pressure waveform fromthe cuff-based module) and the sensor from which the signal originated.This allows the CPU to easily interpret signals that arrive through theCAN connectors 104 a-c, such as those described above corresponding toRR, and means that these connectors are not associated with a specificcable. Any cable connecting to the transceiver can be plugged into anyconnector 104 a-c. As shown in FIG. 4A, the first connector 104 areceives the cable 82 that transports a digitized ECG waveformdetermined from the ECG circuit and electrodes, and digitized ACCwaveforms measured by accelerometers in the sensor module 74 and thebulkhead portion 96 associated with the ECG cable 82.

The second CAN connector 104 b shown in FIG. 5 receives the cable 86that connects to the pneumatic cuff-based system 85 used for thepressure-dependent indexing measurement (shown in FIG. 4A). Thisconnector 104 b receives a time-dependent pressure waveform delivered bythe pneumatic system 85 to the patient's arm, along with values for SYS,DIA, and MAP values determined during the indexing measurement. Thecable 86 unplugs from the connector 104 b once the indexing measurementis complete, and is plugged back in after approximately four hours foranother indexing measurement.

The final CAN connector 104 c can be used for an ancillary device, e.g.a glucometer, infusion pump, body-worn insulin pump, ventilator, oret-CO2 measurement system. As described above, digital informationgenerated by these systems will include a header that indicates theirorigin so that the CPU can process them accordingly.

The transceiver includes a speaker 101 that allows a medicalprofessional to communicate with the patient using a voice over Internetprotocol (VOIP). For example, using the speaker 101 the medicalprofessional could query the patient from a central nursing station ormobile phone connected to a wireless, Internet-based network within thehospital. Or the medical professional could wear a separate transceiversimilar to the shown in FIG. 5, and use this as a communication device.In this application, the transceiver 72 worn by the patient functionsmuch like a conventional cellular telephone or ‘walkie talkie’: it canbe used for voice communications with the medical professional and canadditionally relay information describing the patient's vital signs andmotion. The speaker can also enunciate pre-programmed messages to thepatient, such as those used to calibrate the chest-worn accelerometersfor a posture calculation, as described above.

Other Embodiments of the Invention

RR can also be calculated using a combination of ACC, ECG, PPG, IP, andother signals using algorithms that differ from those described above.For example, these signals can be processed with an averaging algorithm,such as one using a weighted average, to determine a single waveformthat can then be processed to determine RR. Some of these measurementtechniques are described, for example, in the following patientapplication, the contents of which are incorporated herein by reference:‘BODY-WORN MONITOR FOR MEASURING RESPIRATION RATE’ (U.S. Ser. No.12/559,419; filed Sep. 14, 2009). Or the ACC waveform can be used alone,without being integrated in an adaptive filtering algorithm, todetermine RR without relying on IP. In this case the ACC waveform isfiltered with a simple bandpass filter, e.g. a finite impulse responsefilter, with a set passband (e.g. 0.01-5 Hz). Similarly, multiple ACCwaveforms, such as those measured along axes (e.g. the x or y-axes)orthogonal to the vector normal to the patient's chest (i.e. thez-axis), can be processed with or without adaptive filtering todetermine RR. In this case the waveforms may be averaged together with aweighted average to generate a single waveform, which is then filtered,derivatized, and signal processed as described above to determine RR.Similarly, envelopes associated with the ECG and PPG waveforms can beprocessed in a similar manner to determine RR. In still otherembodiments, other sensors, such as detectors for ultra wide-band radaror acoustic microphones, can detect signals indicative of RR and usedwith ACC or IP waveforms and the adaptive filtering approach describedabove to determine RR. Here, the alternative sensors are typically usedto replace measurement of the IP waveform, although they can also beused to replace measurement of the ACC waveform. An acoustic sensorsuitable for this application is described, for example, in thefollowing co-pending patent application, the contents of which areincorporated herein by reference: DEVICE FOR DETERMINING RESPIRATORYRATE AND OTHER VITAL SIGNS (U.S. Ser. No. 12/171,886; filed Jul. 12,2008).

In addition to those methods described above, the body-worn monitor canuse a number of additional methods to calculate blood pressure and otherproperties from the optical and electrical waveforms. These aredescribed in the following co-pending patent applications, the contentsof which are incorporated herein by reference: 1) CUFFLESSBLOOD-PRESSURE MONITOR AND ACCOMPANYING WIRELESS, INTERNET-BASED SYSTEM(U.S. Ser. No. 10/709,015; filed Apr. 7, 2004); 2) CUFFLESS SYSTEM FORMEASURING BLOOD PRESSURE (U.S. Ser. No. 10/709,014; filed Apr. 7, 2004);3) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WEB SERVICESINTERFACE (U.S. Ser. No. 10/810,237; filed Mar. 26, 2004); 4) CUFFLESSBLOOD PRESSURE MONITOR AND ACCOMPANYING WIRELESS MOBILE DEVICE (U.S.Ser. No. 10/967,511; filed Oct. 18, 2004); 5) BLOOD PRESSURE MONITORINGDEVICE FEATURING A CALIBRATION-BASED ANALYSIS (U.S. Ser. No. 10/967,610;filed Oct. 18, 2004); 6) PERSONAL COMPUTER-BASED VITAL SIGN MONITOR(U.S. Ser. No. 10/906,342; filed Feb. 15, 2005); 7) PATCH SENSOR FORMEASURING BLOOD PRESSURE WITHOUT A CUFF (U.S. Ser. No. 10/906,315; filedFeb. 14, 2005); 8) PATCH SENSOR FOR MEASURING VITAL SIGNS (U.S. Ser. No.11/160,957; filed Jul. 18, 2005); 9) WIRELESS, INTERNET-BASED SYSTEM FORMEASURING VITAL SIGNS FROM A PLURALITY OF PATIENTS IN A HOSPITAL ORMEDICAL CLINIC (U.S. Ser. No. 11/162,719; filed Sep. 9, 2005); 10)HAND-HELD MONITOR FOR MEASURING VITAL SIGNS (U.S. Ser. No. 11/162,742;filed Sep. 21, 2005); 11) CHEST STRAP FOR MEASURING VITAL SIGNS (U.S.Ser. No. 11/306,243; filed Dec. 20, 2005); 12) SYSTEM FOR MEASURINGVITAL SIGNS USING AN OPTICAL MODULE FEATURING A GREEN LIGHT SOURCE (U.S.Ser. No. 11/307,375; filed Feb. 3, 2006); 13) BILATERAL DEVICE, SYSTEMAND METHOD FOR MONITORING VITAL SIGNS (U.S. Ser. No. 11/420,281; filedMay 25, 2006); 14) SYSTEM FOR MEASURING VITAL SIGNS USING BILATERALPULSE TRANSIT TIME (U.S. Ser. No. 11/420,652; filed May 26, 2006); 15)BLOOD PRESSURE MONITOR (U.S. Ser. No. 11/530,076; filed Sep. 8, 2006);16) TWO-PART PATCH SENSOR FOR MONITORING VITAL SIGNS (U.S. Ser. No.11/558,538; filed Nov. 10, 2006); and, 17) MONITOR FOR MEASURING VITALSIGNS AND RENDERING VIDEO IMAGES (U.S. Ser. No. 11/682,177; filed Mar.5, 2007).

Other embodiments are also within the scope of the invention. Forexample, other measurement techniques, such as conventional oscillometrymeasured during deflation, can be used to determine SYS for theabove-described algorithms. Additionally, processing units and probesfor measuring pulse oximetry similar to those described above can bemodified and worn on other portions of the patient's body. For example,optical sensors with finger-ring configurations can be worn on fingersother than the thumb. Or they can be modified to attach to otherconventional sites for measuring SpO2, such as the ear, forehead, andbridge of the nose. In these embodiments the processing unit can be wornin places other than the wrist, such as around the neck (and supported,e.g., by a lanyard) or on the patient's waist (supported, e.g., by aclip that attaches to the patient's belt). In still other embodimentsthe probe and processing unit are integrated into a single unit.

In other embodiments, a set of body-worn monitors can continuouslymonitor a group of patients, wherein each patient in the group wears abody-worn monitor similar to those described herein. Additionally, eachbody-worn monitor can be augmented with a location sensor. The locationsensor includes a wireless component and a location-processing componentthat receives a signal from the wireless component and processes it todetermine a physical location of the patient. A processing component(similar to that described above) determines from the time-dependentwaveforms at least one vital sign, one motion parameter, and an alarmparameter calculated from the combination of this information. Awireless transceiver transmits the vital sign, motion parameter,location of the patient, and alarm parameter through a wireless system.A remote computer system featuring a display and an interface to thewireless system receives the information and displays it on a userinterface for each patient in the group.

In embodiments, the interface rendered on the display at the centralnursing station features a field that displays a map corresponding to anarea with multiple sections. Each section corresponds to the location ofthe patient and includes, e.g., the patient's vital signs, motionparameter, and alarm parameter. For example, the field can display a mapcorresponding to an area of a hospital (e.g. a hospital bay or emergencyroom), with each section corresponding to a specific bed, chair, orgeneral location in the area. Typically the display renders graphicalicons corresponding to the motion and alarm parameters for each patientin the group. In other embodiments, the body-worn monitor includes agraphical display that renders these parameters directly on the patient.

Typically the location sensor and the wireless transceiver operate on acommon wireless system, e.g. a wireless system based on 802.11 (i.e.‘WiFi’), 802.15.4 (i.e. ‘Bluetooth’), or cellular (e.g. CDMA, GSM)protocols. In this case a location is determined by processing thewireless signal with one or more algorithms known in the art. Theseinclude, for example, triangulating signals received from at least threedifferent base stations, or simply estimating a location based on signalstrength and proximity to a particular base station. In still otherembodiments the location sensor includes a conventional globalpositioning system (GPS) that processes signals from orbiting satellitesto determine patient's position.

The body-worn monitor can include a first voice interface, and theremote computer can include a second voice interface that integrateswith the first voice interface. The location sensor, wirelesstransceiver, and first and second voice interfaces can all operate on acommon wireless system, such as one of the above-described systems basedon 802.11 or cellular protocols. The remote computer, for example, canbe a monitor that is essentially identical to the monitor worn by thepatient, and can be carried or worn by a medical professional. In thiscase the monitor associated with the medical professional features a GUIwherein the user can select to display information (e.g. vital signs,location, and alarms) corresponding to a particular patient. Thismonitor can also include a voice interface so the medical professionalcan communicate directly with the patient.

In other embodiments, the electrodes connect to the ECG circuit using awireless interface. Here, each electrode includes a small,battery-powered microprocessor, radio, and analog-to-digital converter.The analog-to-digital converter converts an analog signal measured bythe electrode into a digital signal. The radio then sends the digitalsignal to the ECG circuit, which processes signals from multipleelectrodes using a differential amplifier to determine both ECG and IPwaveforms as described above.

FIGS. 3A, 3B show yet another alternate embodiment of the inventionwherein a sensor module 25 attaches to the belly of a patient 10 usingan electrode 24 normally attached to the lower left-hand portion of thepatient's torso. Specifically, the sensor module 25 includes a connector253 featuring an opening that receives the metal snap or rivet presenton most disposable ECG electrodes. Connecting the connector 253 to theelectrode's rivet holds the sensor module 25 in place. Thisconfiguration reduces the number of cables in the body-worn monitor, andadditionally secures an accelerometer 12 to the patient's belly. This istypically the part of their torso that undergoes the greatest motionduring respiration, and thus generates ACC waveforms with the highestpossible signal-to-noise ratio. Also contained within the sensor module25 are the ECG circuit 26, the IP circuit 27, microprocessor 33, and atemperature sensor 34.

To measure IP and ECG waveforms, the sensor module 25 connects throughcables to electrodes 20, 22 attached, respectively, to the upperright-hand and left-hand portions of the patient's torso. This systemmeasures RR using the peak counting, adaptive filtering, and FFT-basedapproaches described above, and has the additional advantage ofmeasuring a relatively large ACC signals indicating respiration-inducedmotions of the patient's belly. As described above, these signals aretypically generated by the z-axis of the accelerometer 12, which isnormal to the patient's torso. ACC signals along the x and y-axes can beadditionally processed to determine the patient's posture and activitylevel, as described above. Once RR and these motion-related propertiesare measured, a transceiver in the sensor module (not shown in thefigure) transmits them in the form of a digital data stream through acable to the wrist-worn transceiver for further processing.

Still other embodiments are within the scope of the following claims.

What is claimed is:
 1. A system for monitoring a patient, comprising: acombined impedance pneumography/ECG/motion sensor comprising a housingconfigured to be worn on the chest of the patient, the housing enclosing(i) at least three electrodes configured to be positioned on thepatient's torso in a triangle configuration; (ii) an impedancepneumography circuit in electrical communication with one of the atleast three electrodes and configured to inject a low amperage, highfrequency current into the patient, and with another of the at leastthree electrodes and configured to measure changes in capacitance of thepatient's thoracic cavity and to generate an analog time-dependentimpedance pneumography waveform, (iii) an ECG circuit in electricalcommunication with the at least three electrodes and configured togenerate at least three analog time-dependent ECG waveforms, (iv) ananalog-to-digital converter configured to convert the analogtime-dependent impedance pneumography waveform into a digitaltime-dependent impedance pneumography waveform and the at least threeanalog time-dependent ECG waveforms into at least three digitaltime-dependent ECG waveforms, (v) an accelerometer measuring motionalong at least three axes and generate therefrom at least threetime-dependent digital motion waveforms corresponding to the at leastthree axes, wherein the accelerometer is positioned within the housingsuch that one of the at least three axes points into the patient'storso, and (vi) a transceiver configured to transmit the time-dependentdigital impedance pneumography waveform, the at least threetime-dependent digital ECG waveforms, and the at least threetime-dependent digital motion waveforms along a common data path via aCAN protocol, wherein each waveform is transmitted with header dataindicating the sensor from which the waveform originates; a processingsystem comprising a microprocessor, a transceiver operably connected tothe microprocessor and the common data path, wherein the transceiver isconfigured to receive via the CAN protocol and relay to themicroprocessor the time-dependent digital impedance pneumographywaveform, the at least three time-dependent digital ECG waveforms, andthe at least three time-dependent digital motion waveforms. atemperature sensor configured to continuously measure a temperature forthe patient.